Consumer Graph for Cross-Device Ad Yield Optimization
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Solution Overview
Problem
Current advertising strategies face challenges in efficiently targeting consumers across multiple devices due to fragmented data, complex media consumption habits, and the inability to integrate disparate data sources, leading to inefficient advertising placement and increased costs.
Innovation Solution
A system and method that utilize a consumer graph to analyze and integrate data from various devices, allowing for probabilistic and deterministic methods to predict consumer behavior and optimize advertising content delivery across TV and mobile devices, enabling more precise targeting and improved yield optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If advertising content is delivered across multiple devices using fragmented data sources, then reach and audience coverage are improved, but measurement precision and targeting accuracy deteriorate
Solution Approach 1:
The patent combines multiple fragmented data sources (TV viewing data, mobile device data, online behavior data) into a unified consumer graph that integrates disparate data points into a cohesive consumer profile. This merging enables accurate cross-device tracking and measurement while maintaining comprehensive audience coverage across multiple devices and platforms.
2Adaptability or versatility
If manual methods are used for advertising strategy development, then flexibility in handling small volumes of purchases is maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The system enables automated self-service advertising optimization through the consumer graph and algorithmic processing. The platform automatically processes advertising strategy development, consumer data integration, and placement optimization without requiring manual analyst intervention, thereby maintaining flexibility while dramatically improving productivity and efficiency.
3Ease of operation
If existing advertising software tools are used for constraint exploration, then ease of operation is maintained, but their ability to process analytics and produce placement suggestions deteriorates
Solution Approach 1:
The consumer graph platform serves multiple functions simultaneously: it acts as a data integration layer, an analytics processing engine, a constraint satisfaction solver, and a placement optimization system. This multi-functional architecture maintains ease of operation while enabling sophisticated analytics processing and automated placement suggestion generation.
4Device complexity
If advertising scheduling problems are solved using traditional methods, then device complexity is kept simple, but their effectiveness in handling combinatorial complexity deteriorates
Solution Approach 1:
The consumer graph serves as an intermediary data structure that simplifies the complex combinatorial advertising scheduling problem. By pre-integrating consumer data across devices and establishing unified consumer profiles, the graph mediates between the complexity of multi-device scheduling and the simplicity required for effective optimization, enabling efficient constraint satisfaction and placement optimization.
Data Source
AI summary
The current invention relates to a computer-generated method for optimizing placement of advertising content across multiple different devices. The system permits targeting of advertising content to consumers on TV and mobile devices that can be operated from within a multi-channel video programming distributor's environment. The system is able to use hard and soft constraints to come up with a number of possible targets for an advertising campaign and can then provide tools for optimizing those targets. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer's device graph to predict future consumption behavior. The system includes an interface through which a user can adjust various constraints and optimize a distributor's revenue yield from advertising.